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Utilizing Depth Based Sensors and Customizable Software Frameworks for Experiential Application

机译:利用基于深度的传感器和可自定义的软件框架进行体验应用

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Using depth sensor cameras such as the Kinect and highly customizable software development frameworks in conjunction with artificial intelligence methodologies offer significant opportunities in a variety of applications, such as undergraduate science, technology, engineering, and math (STEM) education, professional or military training simulation, and individually-tailored cultural and media arts immersion. Designing a participatory educational experience where users are able to actively interface and experience given subject matter in a practical experiential manner can enhance the user's ability to learn and retain presented information. Such natural gesture user interfaces have potential for broad application in disciplines ranging from systems engineering education to process simulation. This paper will discuss progress on the development of testing environments for interactive educational methods in conjunction with artificial intelligent systems that have the ability to adjust the educational user experience based on individual user identification. This will be achieved through depth sensor skeletal tracking, allowing experience adaptation based on the nature and effectiveness of the interactive educational experience.
机译:使用深度传感器相机,如Kinect和高度可定制的软件开发框架与人工智能方法一起提供了各种应用中的重要机会,例如本科科学,技术,工程和数学(Stem)教育,专业或军事训练模拟,单独量身定制的文化和媒体艺术浸没。设计参与式教育体验,用户能够以实际的经验方式主动接口和经验主题,可以提高用户学习和保留所提供信息的能力。这种自然的手势用户界面具有在系统工程教育到处理模拟中的广泛应用程序的潜力。本文将讨论交互式教育方法测试环境的进展,与人工智能系统相结合,可以根据个人用户识别调整教育用户体验的能力。这将通过深度传感器骨架跟踪来实现,允许基于互动教育体验的性质和有效性进行体验。

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